Method and system for modeling and processing vehicular traffic data and information and applying thereof

ABSTRACT

A method and system for modeling and processing vehicular traffic data and information, comprising: (a) transforming a spatial representation of a road network into a network of spatially interdependent and interrelated oriented road sections, for forming an oriented road section network; (b) acquiring a variety of the vehicular traffic data and information associated with the oriented road section network, from a variety of sources; (c) prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources; (d) calculating a mean normalized travel time (NTT) value for each oriented road section of said oriented road section network using the prioritized, filtered, and controlled, vehicular traffic data and information associated with each source, for forming a partial current vehicular traffic situation picture associated with each source; (e) fusing the partial current traffic situation picture associated with each source, for generating a single complete current vehicular traffic situation picture associated with entire oriented road section network; (f) predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network; and (g) using the current vehicular traffic situation picture and the future vehicular traffic situation picture for providing a variety of vehicular traffic related service applications to end users.

[0001] This application in a Continuation of U.S. patent applicationSer. No. 09/939,620 filed Aug. 28, 2001, claims priority from U.S.Provisional Application No. 60/227,905, filed Aug. 28, 2000.

FIELD AND BACKGROUND OF THE INVENTION

[0002] The present invention relates to the field of vehicular trafficdata and information and, more particularly, to a method and system formodeling and processing vehicular traffic data and information, andusing the modeled and processed vehicular traffic data and informationfor providing a variety of vehicular traffic related serviceapplications to end users.

[0003] Despite continuing investing in massive amounts of financial andhuman resources, current road network capacities insufficiently meet theneeds dictated by current levels and growth rates of traffic volume.This dilemma relates to current road network capacities, in general, andcurrent road network capacities in urban, suburban, and rural,environments, in particular. Road congestion, or, equivalently,inconveniently high levels or volumes of vehicular road traffic, is apersistent major factor resulting from this dilemma, and needs to begiven proper attention and taken into account for efficiently schedulingtrips, selecting travel routes, and for attempting to efficientlyallocate and exploit time, by individual drivers, as well as byvehicular traffic logistics personnel such as company vehicular fleetmanagers, responsible for performing such activities. Road congestionand associated traffic data and information also need to be wellunderstood and used by a wide variety of public and private occupationsand personnel, such as designers, planners, engineers, coordinators,traffic law makers and enforcers, directly and/or indirectly involved indesigning, planning, controlling, engineering, coordinating, andimplementing, a wide variety of activities, events, and/or constructionprojects, which depend upon accurate descriptions of current and futurevehicular traffic situations and scenarios. This situation is a maindriving force for the on-going development and application of variousmethods, systems, and devices, for acquiring, analyzing, processing, andapplying, vehicular traffic data and information.

[0004] There are various prior art techniques for acquiring, analyzing,processing, and applying, vehicular traffic data and information. A fewexamples of recent prior art in this field are U.S. Pat. No. 6,236,933,issued to Lang, entitled “Instantaneous Traffic Monitoring System”, U.S.Pat. No. 6,012,012, issued to Fleck et al., entitled “Method And SystemFor Determining Dynamic Traffic Information”, U.S. Pat. No. 6,240,364,issued to Kerner et al., entitled “Method And Device For ProvidingTraffic Information”, and, U.S. Pat. No. 5,845,227, issued to Peterson,entitled “Method And Apparatus For Providing Shortest Elapsed Time RouteAnd Tracking Information To Users”.

[0005] Prior art techniques typically include calculating velocities ofvehicles, for example, by acquiring series of exact locations of thevehicles located along roads in known time intervals, by measuringvehicular traffic flux along roads, especially, along highways, and/or,by a variety of other means known in the field. There are prior arttechniques which are either based on, or, include, the use of networksof fixed or static traffic sensors or electronic devices, Such as videocameras, induction boxes, tag readers, traffic detectors, and so on,which are installed and fixed along known locations of main trafficarteries and/or traffic volume. Fixed or static traffic sensors orelectronic devices, positioned at known locations, relay crossing timesof vehicles to a computerized central traffic data and informationhandling (gathering, collecting, acquiring, analyzing, processing,communicating, distributing) system that consequently calculatesvelocities of the vehicles between two such sensors.

[0006] Significant limitations of developing and implementingcomprehensive, highly accurate and precise, techniques for acquiring,analyzing, processing, and applying, vehicular traffic data andinformation, primarily based upon a system or network of fixed or statictraffic sensors or electronic devices, are the relatively large amountsand expense of the necessary infrastructure and maintenance, especiallyif such resources are to account for and include vehicular traffic dataand information associated with a plethora of minor roads characterizedby low volumes of vehicular traffic.

[0007] More recent prior art techniques are either based on, or, atleast include, the use of mobile sensors or electronic devicesphysically located in or attached to vehicles, each of which is uniquelyor specifically designated or assigned to a particular vehicle, wherebythe mobile sensors or electronic devices automatically transmit vehiclelocations to the computerized central traffic data and informationhandling system according to predetermined time intervals, and, whereby,vehicle velocities are relatively simple to calculate for vehiclelocations acquired with sufficient accuracy.

[0008] For obtaining dynamic vehicle location and velocity data andinformation, having varying degrees of accuracy and precision, fromuniquely or specifically dedicated in-vehicle mobile sensors orelectronic devices, such prior art techniques make use of well knownglobal positioning system (GPS) and/or other types of mobile wirelesscommunication or electronic vehicular tracking technologies, such ascellular telephone or radio types of mobile wireless communicationsnetworks or systems, involving the use of corresponding mobile wirelessdevices such as cellular telephones, laptop computers, personal digitalassistants (PDAs), transceivers, and other types of telemetric devices,which are uniquely or specifically designated or assigned to aparticular vehicle. Establishing and maintaining various communicationsof the mobile sensors or electronic devices, the computerized centraltraffic data and information handling system, and, vehicular end-users,are also performed by mobile wireless communication networks or systems,such as cellular telephone mobile wireless communications networks orsystems, for example, involving the Internet.

[0009] It is noted, however, that due to the requirement of uniquely orspecifically designating or assigning each mobile sensor or electronicdevice to a particular vehicle during the process of gathering,collecting, or acquiring, the vehicular traffic data and information,the potential number of mobile sensors or electronic devices providingdynamic vehicle location and velocity data and information to thecomputerized central traffic data and information handling system islimited, in proportion to the number of vehicles featuring theparticular mobile wireless communication or electronic vehiculartracking technology. For example, currently, there is a significantlylarger potential number of vehicles associated with cellular telephonetypes of a mobile wireless communication network or system compared tothe potential number of vehicles associated with GPS types of a mobilewireless communication network or system.

[0010] Various specific techniques for manually and electronicallygathering, collecting, or acquiring, vehicular traffic data andinformation are relatively well developed and taught about in the priorart. Moreover, various specific techniques for electronicallycommunicating, sending, or distributing, analyzed and processedvehicular traffic data and information in vehicular traffic relatedservice applications to end users are also relatively well developed andtaught about in the prior art. However, there remains a strong on-goingneed for developing better, more comprehensive, highly accurate andprecise, yet, practicable and implementable techniques for analyzing,modeling, and processing, the acquired, collected, or gathered,vehicular traffic data and information. This last aspect is especiallytrue with regard to using vehicular traffic data and information forcomprehensively, yet, accurately and practicably, describing current andpredicting future vehicular traffic situations and scenarios, from whichvehicular traffic data and information are used for providing a varietyof vehicular traffic related service applications to end users.

[0011] In the prior art, a critically important aspect requiring new andimproved understanding and enabling description for developing better,more comprehensive, highly accurate and precise, yet, practicable andimplementable techniques for analyzing, modeling, and processing, theacquired, collected, or gathered, vehicular traffic data andinformation, relates to the use of a geographical information system(GIS), or, other similarly organized and detailed spatial representationof a network of roads, for a particular local or wide area region,within which the vehicular traffic data and information are acquired,collected, or gathered. In particular, there is a need for properly andefficiently ‘spatially’ modeling a road network, and, properly andefficiently ‘spatially’ modeling, interrelating, and correlating, thevehicular traffic data and information which are acquired, collected, orgathered, among a plurality of sub-regions, sub-areas, or, otherdesignated sub-divisions, within the particular local or wide arearegion of the spatially modeled road network. Furthermore, there is aparticular need for incorporating the factor or dimension of time, forproperly and efficiently ‘spatially and temporally’ defining,interrelating, and correlating, the vehicular traffic data andinformation which are acquired, collected, or gathered, among theplurality of sub-regions, sub-areas, or, other designated sub-divisions,within the particular local or wide area region of the spatially modeledroad network.

[0012] In the prior art, another critically important aspect requiringnew and improved understanding and enabling description relates to themodeling and processing of vehicular traffic data and information whichare acquired, collected, or gathered, using techniques based on cellulartelephone types of mobile wireless communications networks or systems,which to date, feature relatively low accuracy and precision of vehiclelocations compared to the less widely used, but significantly morehighly accurate and precise, GPS types of mobile wireless communicationor electronic vehicular tracking technologies.

[0013] In prior art, another critically important aspect requiring newand improved understanding and enabling description relates to themodeling and processing of vehicular traffic data and information whichare acquired, collected, or gathered, from an ‘arbitrary’,non-pre-determined or non-designated, population or group of vehicleseach including a uniquely or specifically designated or assigned mobilesensor or electronic device, therefore, resulting in a potentially largenumber of mobile sensors or electronic devices providing dynamic vehiclelocation and velocity data and information to the computerized centraltraffic data and information handling system.

[0014] In the prior art, another critically important aspect requiringnew and improved understanding and enabling description relates to theproper and efficient combining or fusing of a variety of vehiculartraffic data and information which are acquired, collected, or gathered,using a combination of a various techniques based on networks of fixedor static traffic sensors or electronic devices, GPS and/or cellulartelephone types of mobile wireless communications networks or systems,and, various other manual and electronic types of vehicular traffic dataand information such as historical and/or event related vehiculartraffic data and information.

[0015] In the prior art, another important aspect requiringunderstanding and enabling description relates to techniques forprotecting the privacy of individuals associated with or hosting thesources, that is, the mobile sensors or electronic devices, of vehiculartraffic data and information which are acquired, collected, or gathered,using techniques based on GPS and/or cellular telephone types of mobilewireless communications networks or systems. The inventors are unawareof any prior art teaching for performing this in the field of vehiculartraffic data and information.

[0016] There is thus a strong need for, and it would be highlyadvantageous to have a method and system for modeling and processingvehicular traffic data and information, and using the modeled andprocessed vehicular traffic data and information for providing a varietyof vehicular traffic related service applications to end users.Moreover, there is a particular need for such a generally applicablemethod and system with regard to using vehicular traffic data andinformation for comprehensively, yet, accurately and practicably,describing current and predicting future vehicular traffic situationsand scenarios, from which vehicular traffic data and information areused for providing the variety of vehicular traffic related serviceapplications to the end users.

SUMMARY OF THE INVENTION

[0017] The present invention relates to a method and system for modelingand processing vehicular traffic data and information, and using themodeled and processed vehicular traffic data and information forproviding a variety of vehicular traffic related service applications toend users. The present invention especially includes features for usingvehicular traffic data and information for comprehensively, yet,accurately and practicably, describing current and predicting futurevehicular traffic situations and scenarios, from which vehicular trafficdata and information are used for providing the variety of vehiculartraffic related service applications to the end users.

[0018] Thus, according to the present invention, there is provided amethod and a system for modeling and processing vehicular traffic dataand information, comprising: (a) transforming a spatial representationof a road network into a network of spatially interdependent andinterrelated oriented road sections, for forming an oriented roadsection network; (b) acquiring a variety of the vehicular traffic dataand information associated with the oriented road section network, froma variety of sources; (c) prioritizing, filtering, and controlling, thevehicular traffic data and information acquired from each of the varietyof sources; (d) calculating a mean normalized travel time (NTT) valuefor each oriented road section of said oriented road section networkusing the prioritized, filtered, and controlled, vehicular traffic dataand information associated with each source, for forming a partialcurrent vehicular traffic situation picture associated with each source;(e) fusing the partial current traffic situation picture associated witheach source, for generating a single complete current vehicular trafficsituation picture associated with entire oriented road section network;(f) predicting a future complete vehicular traffic situation pictureassociated with the entire oriented road section network; and (g) usingthe current vehicular traffic situation picture and the future vehiculartraffic situation picture for providing a variety of vehicular trafficrelated service applications to end users.

[0019] The present invention successfully overcomes all the previouslydescribed shortcomings and limitations of presently known techniques foranalyzing, modeling, and processing, the acquired, collected, orgathered, vehicular traffic data and information. Especially with regardto using vehicular traffic data and information for comprehensively,yet, accurately and practicably, describing current and predictingfuture vehicular traffic situations and scenarios, from which vehiculartraffic data and information are used for providing a variety ofvehicular traffic related service applications to end users. Anotherimportant benefit of the present invention is that it is generallyapplicable and complementary to various different ‘upstream’ prior arttechniques of gathering, collecting, or acquiring, vehicular trafficdata and information, and, generally applicable and complementary tovarious different ‘downstream’ prior art techniques of electronicallycommunicating, sending, or distributing, the analyzed, modeled, andprocessed, vehicular traffic data and information in vehicular trafficrelated service applications to end users.

[0020] Implementation of the method and system for modeling andprocessing vehicular traffic data and information, and using the modeledand processed vehicular traffic data and information for providing avariety of vehicular traffic related service applications to end users,according to the present invention, involves performing or completingselected tasks or steps manually, automatically, or a combinationthereof. Moreover, according to actual instrumentation and/or equipmentused for implementing a particular preferred embodiment of the disclosedinvention, several selected steps of the present invention could beperformed by hardware, by software on any operating system of anyfirmware, or a combination thereof. In particular, as hardware, selectedsteps of the invention could be performed by a computerized network, acomputer, a computer chip, an electronic circuit, hard-wired circuitry,or a combination thereof, involving any number of digital and/or analog,electrical and/or electronic, components, operations, and protocols.Additionally, or alternatively, as software, selected steps of theinvention could be performed by a data processor, such as a computingplatform, executing a plurality of computer program types of softwareinstructions or protocols using any suitable computer operating system.

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The invention is herein described, by way of example only, withreference to the accompanying drawings, wherein:

[0022]FIG. 1 is a block flow diagram of a preferred embodiment of themethod and system for modeling and processing vehicular traffic data andinformation, and using the modeled and processed vehicular traffic dataand information for providing a variety of vehicular traffic relatedservice applications to end users, in accordance with the presentinvention;

[0023]FIG. 2 is a schematic diagram illustrating a preferred embodimentof modeling a road network in terms of oriented road sections' as partof, and in relation to, oriented road section network 14 ofmethod/system 10 of FIG. 1, in accordance with the present invention;

[0024]FIG. 3 is a pictorial diagram illustrating exemplary resultsobtained from the process of path identification using vehicular trafficdata and information acquired from mobile sensors of a cellular phonemobile communication network, in accordance with the present invention;

[0025]FIG. 4 is a pictorial diagram illustrating exemplary resultsobtained from the process of path identification using vehicular trafficdata and information acquired from mobile sensors of an anti-theftmobile communication network, in accordance with the present invention;

[0026]FIG. 5 is a schematic diagram illustrating a partial currentvehicular traffic situation picture featuring current NTT valuesobtained from an exemplary network source of mobile sensor vehiculartraffic data and information, and indicated for each oriented roadsection of an exemplary part of an oriented road section network, inaccordance with the present invention;

[0027]FIG. 6 is a graph illustrating an example of vehicular trafficbehavior associated with an exemplary oriented road section, in terms ofdifferent NTT values plotted as a function of time, in accordance withthe present invention;

[0028]FIG. 7 is a graphical diagram illustrating usage of athree-dimensional vehicular traffic situation picture for providingroute recommendation types of traffic related service applications toend users, in accordance with the present invention; and

[0029]FIG. 8 is a graphical diagram illustrating usage of athree-dimensional vehicular traffic situation picture for providingtraffic alerts and alternative route recommendation types of trafficrelated service applications to end users, in accordance with thepresent invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] The present invention takes priority from U.S. Provisional PatentApplication No. 60/227,905, filed Aug. 28, 2000, entitled “DynamicTraffic Flow Forecasting, Using Large Volumes Of Privacy ProtectedLocation Data”, the teachings of which are incorporated by reference asif filly set forth herein.

[0031] The present invention relates to a method and system for modelingand processing vehicular traffic data and information, and using themodeled and processed vehicular traffic data and information forproviding a variety of vehicular traffic related service applications toend users. The present invention especially includes features for usingvehicular traffic data and information for comprehensively, yet,accurately and practicably, describing current and predicting futurevehicular traffic situations and scenarios, from which vehicular trafficdata and information are used for providing the variety of vehiculartraffic related service applications to the end users.

[0032] The present invention features several aspects of novelty andinventive step over the prior art, for developing better, morecomprehensive, highly accurate and precise, yet, practicable andimplementable techniques for analyzing, modeling, and processing, theacquired, collected, or gathered, vehicular traffic data andinformation. Several main aspects of the present invention are brieflydescribed herein. These and additional aspects of the present inventionare described in more detail thereafter.

[0033] One main aspect of the present invention relates to theadaptation and modeling of a geographical information system (GIS), or,other similarly organized and detailed spatial representation of anetwork of roads, for a particular local or wide area region, withinwhich the vehicular traffic data and information are acquired,collected, or gathered. In particular, there is efficiently ‘spatially’modeling the road network, in order to accommodate and fit the varietyof vehicular traffic data and information, while limiting the amount ofdata and information needed to be stored and handled. By way of thisadaptation and modeling of a road network, especially a GIS type of roadnetwork, there is also properly and efficiently ‘spatially’ modeling,interrelating, and correlating, the vehicular traffic data andinformation which are acquired, collected, or gathered, among aplurality of sub-regions, sub-areas, or, other designated sub-divisions,within the particular local or wide area region of the spatially definedroad network. Furthermore, there is incorporating the factor ordimension of time, for properly and efficiently ‘spatially andtemporally’ defining, interrelating, and correlating, the vehiculartraffic data and information which are acquired, collected, or gathered,among the plurality of sub-regions, sub-areas, or, other designatedsub-divisions, within the particular local or wide area region of thespatially defined road network.

[0034] Another main aspect of the present invention relates to themodeling and processing of vehicular traffic data and information whichare acquired, collected, or gathered, using techniques based on mobilewireless communications networks or systems, such as cellular telephonetypes of networks and systems, which to date, feature relatively lowaccuracy and precision of vehicle locations compared to the less widelyused, but significantly more highly accurate and precise, GPS types ofmobile wireless communication or electronic vehicular trackingtechnologies.

[0035] Another main aspect of the present invention relates to themodeling and processing of vehicular traffic data and information whichare acquired, collected, or gathered, from an ‘arbitrary’,non-pre-determined or non-designated, population or group of vehicleseach including a uniquely or specifically designated or assigned mobilesensor or electronic device, therefore, resulting in a potentially largenumber of mobile sensors or electronic devices providing dynamic vehiclelocation and velocity data and information to the computerized centraltraffic data and information handling system.

[0036] Another main aspect of the present invention relates to efficientcombining or fusing of a variety of vehicular traffic data andinformation which are acquired, collected, or gathered, using acombination of various techniques based on networks of fixed or statictraffic sensors or electronic devices, mobile wireless communicationsnetworks or systems such as GPS and/or cellular telephone networks orsystems, and, various other manual and electronic types of vehiculartraffic data and information such as traffic reports, incorporating inthe fusion process the historical and/or event related vehicular trafficdata and information that is accumulated, analyzed, and processed, frompreviously accumulated vehicular traffic data and information in thesame system.

[0037] Another main aspect of the present invention relates totechniques for protecting the privacy of individuals associated with orhosting the sources, that is, the mobile sensors or electronic devices,of vehicular traffic data and information which are acquired, collected,or gathered, using techniques based on GPS and/or cellular telephonetypes of mobile wireless communications networks or systems. Theinventors are unaware of any prior art teaching for performing this inthe field of vehicular traffic data and information.

[0038] It is to be understood that the present invention is not limitedin its application to the details of the order or sequence of steps ofoperation or implementation of the method, or, to the details ofconstruction and arrangement of the various devices and components ofthe system, set forth in the following description and drawings. Forexample, the following description refers to a cellular telephone typeof mobile wireless communication network or system as the primary sourceof electronically acquired vehicular data and information, in order toillustrate implementation of the present invention. As indicated below,additionally, or, alternatively, other types of mobile wirelesscommunication networks or systems function as sources of electronicallyacquired vehicular data and information. Accordingly, the invention iscapable of other embodiments or of being practiced or carried out invarious ways. Also, it is to be understood that the phraseology andterminology employed herein are for the purpose of description andshould not be regarded as limiting.

[0039] Steps, components, operation, and implementation of a method andsystem for modeling and processing vehicular traffic data andinformation, and using the modeled and processed vehicular traffic dataand information for providing a variety of vehicular traffic relatedservice applications to end users, according to the present inventionare better understood with reference to the following description andaccompanying drawings. Throughout the following description andaccompanying drawings, like reference numbers refer to like elements.

[0040] Referring now to the drawings, FIG. 1 is a block flow diagram ofa preferred embodiment of the method and system, hereinafter, generallyreferred to as method/system 10, of the present invention. Terminologyand referenced items appearing in the following description of FIG. 1are consistent with those used in FIGS. 1 through 8.

[0041] In Step (a) of the method of the present invention, there istransforming a spatial representation of a road network into a networkof spatially interdependent and interrelated oriented road sections, forforming an oriented road section network.

[0042] Specifically, there is transforming a spatial representation of aroad network 12 into a network of spatially interdependent andinterrelated oriented road sections, for forming an oriented roadsection network 14. Preferably, road network 12 is a geographicalinformation system (GIS) type of road network 12, which is well known inthe art of vehicular road traffic data and information. Typically, a GIStype of road network 12 is a detailed spatial representation of a roadnetwork encompassing a particular local or wide area region, withinwhich are a plurality of sub-regions, sub-areas, or, other designatedsub-divisions, such as roads, turns, junctions, and, areas or regions ofvariably populated streets and roads. Associated with road network 12,and consequently, oriented road section network 14, are the vehiculartraffic data and information which are acquired, collected, or gathered,among the plurality of sub-regions, sub-areas, or, other designatedsub-divisions. Step (a) corresponds to an initialization step whenmethod/system 10 is applied to a new geographical region or road network12. Vehicular traffic data and information are subsequently added, byvarious manual and/or electronic means, to the oriented road sectionnetwork 14 model of a GIS type of road network 12.

[0043] The approach of the present invention to the field of vehiculartraffic data and information, in general, and to the problematicsituation of vehicular traffic congestion, in particular, is from a‘holistic’ point of view, emphasizing the importance of an entire roadnetwork 12 encompassing a particular local or wide area region, and theinterdependence and interrelation of the plurality of sub-regions,sub-areas, or, other designated sub-divisions of road network 12. Thisis accomplished by adapting and transforming a spatial representation ofroad network 12 into a network of spatially interdependent andinterrelated oriented road sections, for forming oriented road sectionnetwork 14. This process is conceptually analogous to transforming roadnetwork 12 into an intertwined weave of oriented road sections, forforming oriented road section network 14.

[0044]FIG. 2 is a schematic diagram illustrating a preferred embodimentof modeling a road network in terms of ‘oriented road sections’ as partof, and in relation to, oriented road section network 14 ofmethod/system 10 of FIG. 1. In the upper part of FIG. 2, direction ofvehicular traffic flow is indicated by the arrows drawn inside the roadsegments and inside the road junctions. Herein, the term ‘road section’,in general, represents a unit featuring at least one consecutive roadsegment of a road network, preferably, a GIS type of road network, wherethe at least one road segment, positioned head-to-tail relative to eachother, are located between two road junctions, referred to as a tail endroad junction and a head end road junction, within the road network, andare characterized by similar vehicular traffic data and information. Inpractice, typically, a ‘road section’ represents a unit featuring a setof a plurality of consecutive road segments of a road network,preferably, a GIS type of road network, where the consecutive roadsegments, positioned head-to-tail relative to each other, are locatedbetween two road junctions, a tail end road junction and a head end roadjunction, within the road network, and are characterized by similarvehicular traffic data and information. For purposes of effectivelymodeling a particular road network, a plurality of road segments aretherefore combined into a single homogeneous or uniform road sectioncharacterized by homogeneous or uniform vehicular traffic data andinformation. Accordingly, the road network is modeled as a plurality ofroad sections.

[0045] Accordingly, in the upper part of FIG. 2, generally indicated as15, a road section, as a first example, road section 16, represents aunit featuring a set of a plurality of, for example, two, consecutiveroad segments 18 and 20 of a road network (12 in FIG. 1, partly shown inFIG. 2), where consecutive road segments 18 and 20 are positionedhead-to-tail relative to each other, are located between two roadjunctions, in particular, head end road junction 22 and tail end roadjunction 24, within the road network, and are characterized by similarvehicular traffic data and information. As a second example, roadsection 26, represents a unit featuring a set of a plurality of, forexample, three, consecutive road segments 28, 30, and, 32, within thesame road network (12 in FIG. 1, partly shown in FIG. 2), whereconsecutive road segments 28, 30, and, 32, are positioned head-to-tailrelative to each other, are located between two road junctions, inparticular, head end road junction 34 and tail end road junction 22,within the road network, and are characterized by similar vehiculartraffic data and information.

[0046] Two additional examples, relating to the two directions of abidirectional street, of a road section, are road section 36 and roadsection 38, each representing a single consecutive road segment 40,within the road network (12 in FIG. 1, partly shown in FIG. 2), whereconsecutive road segment 40 is positioned head-to-tail (relative toitself and oppositely for each of the two directions), located betweentwo road junctions, in particular, head end road junction 42 and tailend road junction 22, and, head end road junction 22 and tail end roadjunction 42, respectively, within the road network, and each ischaracterized by a particular vehicular traffic data and information,according to each of the two directions, respectively.

[0047] Herein, the term ‘oriented road section’ represents a roadsection having a single vehicular traffic continuation option located atthe head end road junction. Herein, a single vehicular trafficcontinuation option refers to one of the various vehicular traffic flowoptions a vehicle may take, such as optionally continuing to travelstraight, optionally taking a right turn, or, optionally taking a leftturn, from a particular road segment joined or linked to the head endroad junction. Accordingly, a vehicular traffic continuation option isselected from the group consisting of continuing to travel straight,taking a right turn, and, taking a left turn, from a particular roadsegment joined or linked to the head end road junction.

[0048] Accordingly, in the lower part of FIG. 2, generally indicated as50, shown are eight oriented road sections, that is, oriented roadsections 16 a, 16 b, 16 c, associated with road section 16; orientedroad sections 26 a and 26 b, associated with road section 26; orientedroad section 36 a, associated with road section 36; and, oriented roadsections 38 a and 38 b, associated with road section 38. Each orientedroad section has a single vehicular traffic continuation option, thatis, a vehicular traffic continuation option selected from the groupconsisting of continuing to travel straight, taking a right turn, and,taking a left turn, from the particular road segment joined or linked tothe associated head end road junction, as shown by the arrows in thelower part of FIG. 2, corresponding to the arrows in the upper part ofFIG. 1, indicating direction of vehicular traffic flow drawn inside theroad segments and inside the road junctions.

[0049] An equally alternative way of defining the term oriented roadsection is that, a road section having a head end road junction with aparticular plurality of vehicular traffic continuation options is splitor divided into that particular plurality of oriented road sections.Accordingly, in FIG. 2, for example, road section 16 having head endroad junction 22 with a plurality of three vehicular trafficcontinuation options, that is, vehicular traffic continuation option 16a (continuing to travel straight), 16 b (taking a right turn), and, 16 c(taking a left turn), is split or divided into a plurality of threeoriented road sections 16 a, 16 b, and 16 c, respectively.

[0050] When special lanes in the various road segments within theoriented road section network are assigned to turning traffic, therespective oriented road sections may yield different vehicular trafficdata and information, such as different values relating to roadcongestion or heavy vehicular traffic volume. This representation of aroad network enables the incorporation of interdependence andinterrelation among the plurality of road segments, the plurality ofroad sections, and, the plurality of oriented road sections. Inparticular, the oriented road section network model of a road networkaccounts for the significant influence of road junctions on vehiculartraffic flow and associated travel time delays. Moreover, there isstrong interdependence and interrelation between any given particularoriented road section characterized by a particular traffic situation orscenario and other oriented road sections in the same vicinity, eithercrossing or parallel to the particular oriented road section.

[0051] In Step (b), there is acquiring a variety of vehicular trafficdata and information associated with the oriented road section network,from a variety of sources.

[0052] An important aspect of the present invention is the ability tomodel and process a wide variety of vehicular traffic data andinformation, in particular, which are used for generating current andfuture vehicular traffic situation pictures. Referring to method/system10 of FIG. 1, sources of vehicular traffic data and information areselected from the group consisting of sources 60 of fixed sensors,sources 62 of mobile sensors, sources 64 of traffic reports by police orradio broadcasts of vehicular traffic data and information, othersources 66, and combinations thereof. Each of the variety of acquired,collected, or, gathered, vehicular traffic data and information, ischaracterized by a variable level of accuracy, and is independent of anyother specific characteristics of the corresponding source.

[0053] Systems of fixed sensors and traffic reports of historical and/orevent related vehicular traffic data and information are presently themost common sources of vehicular traffic data and information, and arewell known in the art of vehicular traffic data and information.However, the present invention features mobile sensors as mostadvantageous for acquiring vehicular traffic data and information, eventhough they have lower confidence levels. A growing number of mobilewireless communication devices are increasingly being installed orcarried in vehicles, and are capable of transmitting vehicular locationsto a computerized central data and information receiving and processingsystem. Such mobile wireless communication devices are telemetricdevices with GPS capabilities, anti-theft devices, and, driver-carrieddevices such as computer laptops and cellular phones. The respectivecomputerized central data and information receiving and processingsystems have capabilities of locating these devices, and act as sourcesof vehicular traffic data and information for assessing trafficsituations. The variability of these devices and the spreadingpopularity of some of them leads to an abundant and widespreadpopulation of mobile sensors. However, some of these systems, such as acellular telephone wireless communication networks, have poor locationaccuracy, and therefore require elaborate and unique processing in orderto extract useful vehicular traffic data and information.

[0054] In principle, method/system 10 of the present invention isapplicable to all types of mobile sensor systems, and models andprocesses the variability of location accuracy, vehicular trafficmovement data and information, reading time intervals, and other typesof vehicular traffic data and information as further described below.Method/system 10 acquires vehicular traffic data and information fromseveral such sources in parallel and combines or fuses the acquiredvehicular traffic data and information into one coherent and completevehicular traffic situation picture.

[0055] Acquiring the vehicular traffic data and information from amobile sensor system is performed by tracking a sample of mobile sensors62 (FIG. 1) that are carried in moving vehicles. Locations of mobilesensors 62 are obtained from the mobile network in known time intervals,and accordingly, the path of the vehicle is identified in terms of aplurality of oriented road sections featured in the oriented roadsection network, for example, the plurality of exemplary oriented roadsections shown in FIG. 2 featured in oriented road section network 14(FIG. 1), and the velocity of a given vehicle on the different roadsections of the identified path are calculated. A statistical processingof the velocities of all mobile sensors 62 that traveled on a specificoriented road section during the time period of an assessment cycleyields a normalized travel time (NTT) value, hereinafter, also referredto as an NTT value, on that specific oriented road section, where thenormalized travel time refers to a travel time normalized with respectto a pre-determined distance, for example, in a non-limiting fashion,normalized with respect to a distance having a range of between about 10meters to about 100 meters, preferably, a distance of 100 meters. Whenmore than one such NTT value is calculated, a possibility of differentvelocities on different lanes of a particular oriented road section isindicated.

[0056] In Step (c), there is prioritizing, filtering, and controlling,the vehicular traffic data and information acquired from each of thevariety of sources.

[0057] In this step of implementing method/system 10, consideration isgiven to various aspects relating to prioritizing, filtering, andcontrolling, the vehicular traffic data and information acquired fromeach of the variety of sources. For example, filtering noisecorresponding to irrelevant sensors and erroneous data.

[0058] With respect to the choice of the sampled mobile sensor devices,sampling policy or prioritizing the variety of vehicular traffic dataand information, and controlling the sampling of the vehicular trafficdata and information, statistical consideration show that a very smallpercentage, approximately 1-2%, of moving vehicles provide a sufficientbase to obtain the necessary data. The number of moving sensors is,however, huge, and there is need to efficiently choose the samplepopulation, to decide on a sampling policy, and to control its carryingout, so that the data collected will be as relevant as possible for thepurpose of vehicular traffic assessment.

[0059] The choice of sample units is done by making sure that they arein high probability moving-vehicle-carried. In cellular telephonenetworks, this is done by identifying phones whose cell-handover ratesindicate a relatively fast movement. A parallel procedure is performedfor any mobile sensor source. In mobile anti-theft systems, for example,vehicular ignition activation is a valid indicator of‘movement-suspicion’. This initial choice of sensor population assures,for example, that pedestrians will not be included, and so prevents,along the track, the confusion between walking pedestrians and slowingtraffic due to heavy congestion.

[0060] Data acquisition from cellular phones is controlled by a serverconnected to a particular cellular phone network. The operation ofcellular phones provokes a large amount of administrative and controlmessages that flow in the cellular phone network whenever any cellular‘event’ occurs. For example, initiation of a call, completion of a call,transmission of a short message service (SMS) message, a cell transitionor handover, etc. The server of the cellular phone network monitorsthese messages and intercepts those that indicate ‘movement’ of cellularphone holders, for example, phones whose cell handovers rate can pointto a vehicle speed.

[0061] Those cellular phones that are suspected as being‘vehicle-carried’ are immediately tracked for their locations using thehandovers themselves as ‘footprints’ or ‘footsteps’, and/or otherlocating capability of the cellular phone network. Different particularcellular phone networks use different locating techniques and systems,each having a different characteristic locating accuracy. Method/system10 is independent of specific locating techniques and systems, and issuited to a variability of accuracy and precision levels. For example, acommon difficulty concerning locating accuracy and precision of a mobilesensor network is the phenomenon of ‘noise’, such as that caused byreflections, low cell-efficiency management, and/or, even errors. As aresult of this, a substantial number of the footprints is erroneous,whereby they do not represent real or actual sensor locations.

[0062] The tracking of the ‘moving’ phones is done by polling theirlocations in known time intervals. This operation is controlled bySampler software module 1 indicated in FIG. 1. Sampler software module 1regulates the sampling so that it will be most efficient. Specifically,for example, Sampler software module 1 (i) filters out cellular phonesthat are recognized as ‘noise’, (ii) prioritizes the vehicular trafficdata and information acquisition according to policy or presentvehicular traffic circumstances, (iii) prevents tracking cellular phonesthat stopped moving, and, (iv) maintains the sampling procedure withinthe locating capacity of the particular cellular phone network.

[0063] Once the ‘moving’ sensors are identified, method/system 10includes them in the sample population and controls the flow of thevehicular traffic data and information according to actual features andcapabilities of method/system 10, in general, and of Sampler softwaremodule 1, in particular, and, according to changing locations of themoving sensors along their respective paths. For example, Samplersoftware module 1 obtains the vehicular traffic data and information by‘push’ or ‘pull’ procedures, that is, where ‘push’ is a mode ofreceiving the vehicular traffic data and information that is initiatedby source 62 of the mobile sensors, and, where ‘pull’ is a mode ofreceiving the vehicular traffic data and information when the initiatoris Sampler software module 1. This tracking operation is controlled bymethod/system 10 according to a predefined and updated policy. Forinstance, according to a policy of ‘not to track too many vehicles in asame region’, ‘focus the tracking on a certain problematic region’,‘stop tracking a vehicle that stopped for a pre-determined tineinterval’, and, ‘collect vehicular traffic data and information within acertain limited capacity of the cellular phone network’.

[0064] As previously indicated, a main aspect of the present inventionrelates to techniques for protecting the privacy of individualsassociated with, or hosting, the sources 62 (FIG. 2) of the mobilesensors or electronic devices, of the vehicular traffic data andinformation which are acquired, collected, or gathered, using techniquesbased on GPS and/or cellular telephone types of mobile wirelesscommunications networks or systems. Tracking vehicles without theknowledge and consent of their drivers may be considered as violation ofprivacy. Method/system 10, in general, and technical architecture ofmethod/system 10, in particular, are designed for deleting identities ofthe associated sample units, and for deleting individual sensor tracksonce their derived velocities are incorporated into calculations andprocessing associated with the plurality of oriented road sectionswithin oriented road section network 14.

[0065] Moreover, these processing steps take place in a server that isconnected to the mobile sensor source network. When more than one mobilesensor source provides vehicular traffic data and information tomethod/system 10, such a server is in communication with each of thedata providers. Tracked vehicles are not identified by using phonenumbers. Even within the short tracking times, mobile sensor identitiesare kept within the location of the cellular phone network, so thatduring the whole process, privacy of cellular phone users is protected.

[0066] In Step (d), there is calculating a mean normalized travel time(NTT) value for each oriented road section of the oriented road sectionnetwork using the prioritized, filtered, and controlled, vehiculartraffic data and information associated with each source, for forming apartial current vehicular traffic situation picture associated with eachsource.

[0067] Prioritized, filtered, and controlled, vehicular traffic data andinformation associated with each source are transmitted from Samplersoftware module 1 to NTT calculator software module 2, as indicated inFIG. 1. In this step of implementing method/system 10 (FIG. 1),consideration is given to assessment of normalized travel times (NTTs)values on oriented road sections, from individual calculated values ofNTTs, and, to inaccuracy problems relating to identifying the path of asample, and in extracting and calculating the NTT values.

[0068] Data from fixed sensors are usually received in terms of velocityor NTT values. Transition of these values for processing according tothe model of method/system 10 of the present invention isstraightforward to one of ordinary skill in the art, and is not furtherdescribed herein. Vehicular traffic data and information obtained fromtraffic reports are handled manually and used for determining specificNTT values on oriented road sections or the specific patterns to beused. Vehicular traffic data and information from mobile sensor sourcesis processed in a more elaborate way. As previously indicated, above, inthis description of the preferred embodiments, source 62 of theplurality of mobile sensors is represented as a cellular phone network,being the most complex one, but is generally applicable to other mobilewireless communication networks or systems operating with a plurality ofmobile sensors or electronic devices.

[0069] Specifically applicable to vehicular traffic data and informationassociated with the mobile cellular phone network source, there isidentifying a path taken by each vehicle and calculating a meannormalized travel time (NTT) value for each oriented road section oforiented road section network 14 (FIG. 1) using the prioritized,filtered, and controlled vehicular traffic data and information, forforming a partial current vehicular traffic situation picture associatedwith the cellular phone network source.

[0070] Location readings acquired from mobile sensor networks are the‘footprints’ of the sensor's track. When footprints are inaccurate, theyare represented by geometrical areas, whose form and size depend on theparticular features of the locating method. In cellular phone networks,footprints can be the cells themselves, with or without time-advancedata, and their graphic representation is by segments of circle sectorswhose coverage can reach dimensions between tens and thousands ofmeters. Another type of footprint associated with cellular phonenetworks is a cell-handover, whose graphic representation is dictated bythe relative position of the two cells involved in the ‘handover’.

[0071] Specific characteristics of the location data obtained from thevariety of mobile sensor networks varies with the network, whereby,method/system 10 accounts for them in a generic way, by implementingstatistical methods and algorithms that rely on some assumptions as tothe reasonable behavior of vehicular drivers. The path of the vehicle isdeduced out of several consecutive readings. The deduced path is thesequence of connected oriented road sections that cross the locationareas in the order of their appearance, and which join into a logicalroute to take between two points. In this step, some of the ‘noise’ isdiscovered, for example, footprints that went beyond bounds of a‘sensible’ travel route. Additionally, those mobile sensors that are notrelevant, for example, those carried in trains, are also identified.Those single footprints or irrelevant sample units are immediatelyrejected and their tracking is stopped.

[0072] The NTTs of the vehicle on every section of the deduced path iscalculated using the timings of the footprints. The vehicle's positionon the road at the reading's moment is determined by introducing someadditional assumptions, like minimal acceleration and minimal velocity.The size of the footprints of a specific source influences theresolution of the oriented road sections that can be valued. Actually,the method emphasizes the more congested roads, because theystatistically offer more readings. Those are identified even if they areminor roads, but, with worse accuracy. However, there will always besmaller streets and roads that this system will fail to exactlyidentify. Those areas of smaller streets, confined and closed withinhigher level arteries of the road network are referred to as ‘regions’in the road network. Method/system 10 identifies ‘passage’ of the mobilesensor in such a region, and determines the average NTT value in thatregion. Indetermination of specific streets within regions of the roadnetwork is insignificant for the purpose of implementing method/system10, because they usually behave in a similar way, other vise specificstreets that are more congested would stand out in the first place.

[0073] NTT values on an individual oriented section are calculated usingthe results obtained from all mobile sensors that passed that orientedroad section, hence resulting in statistically determined NTT values foreach oriented road section. The unification of the individual NTT valuesinto a determined value per oriented road section is done withconsideration of the confidence factor of each of the individual data.This confidence factor is a function of the accuracy, the amount offootprints, the error rate, and so on.

[0074] In the transition process from individual to comprehensive NTTvalues, an additional filtering stage takes place. Irregularities ofsome sensors may again indicate irrelevancy (stationed or slow movingvehicle, stop-and-go vehicles like trash collectors, etc.) or error.Data from those mobile sensors are rejected and filtered out of thecontinuation in the processing of the data. The outcome of this step inmethod/system 10 is an NTT picture of the oriented road sections for acertain time stamp, as calculated and assessed out of the specificnetwork of mobile sensors. Accordingly, there is forming a partialcurrent vehicular traffic situation picture associated with each networksource of mobile sensors.

[0075] As indicated in FIG. 1, prioritized, filtered, and controlled,vehicular traffic data and information associated with each source aretransmitted from Sampler software module 1 to NTT calculator softwaremodule 2. NTT calculator software module 2 identifies the path taken byevery vehicle from the consecutive footsteps of the mobile sensorassociated with each vehicle. A small number of low-accuracy locationscan fit several possible paths, but with each additional footstep of thevehicle, the number of path alternatives is reduced, and the path isdetermined by method/system 10 only when the set of footsteps points toone path in high probability. The probability influences theconfidentiality of the path.

[0076]FIG. 3 is a pictorial diagram illustrating exemplary resultsobtained from the process of path identification using vehicular trafficdata and information acquired from mobile sensors of a cellular phonemobile communication network, and, FIG. 4 is a pictorial diagramillustrating exemplary results obtained from the process of pathidentification using vehicular traffic data and information acquiredfrom mobile sensors of an anti-theft mobile communication network. Forpurposes of clarity, background 70 in FIG. 3, and, background 80 in FIG.4, each corresponds to a GIS type representation of road network 12(FIG. 1).

[0077] Sectors, for example, sectors 72, 74, and, 76, in FIG. 3, and,squares, for example, squares 82, 84, and, 86, in FIG. 4, represent thefootprints corresponding to the tracking data obtained from the twodifferent types of mobile communication networks, that is, from acellular phone mobile communication network, and, from an anti-theftmobile communication network, respectively. Location accuracy in FIGS. 3and 4 is approximately 0.5-1.0 km. In FIGS. 3 and 4, the path identifiedby NTT calculator software module 2 of method/system 10 is indicated bythe dark lined path, that is, dark lined path 90 in FIG. 3, and, darklined path 100 in FIG. 4, which is exactly the one taken by the vehicle,as shown by highly accurate readings, indicated by the set of smallsolid diamonds 92 in FIG. 3, and, by the set of small solid circles 102in FIG. 4, obtained from a GPS tracking system serving as control data.

[0078] Once a path is identified, the normalized travel times (NTTs)values between the footsteps are calculated, hence, NTT values of theplurality of oriented road sections for an individual vehicle areassessed. At this stage, the determined path of the mobile sensor may beidentified as crossing a ‘region’ of streets having low vehiculartraffic congestion. Ordinarily, footprint accuracy preventsidentification of specific streets in such a region. NTT calculatorsoftware module 2 processes such regions as a special type of orientedroad section and allocates the region with a calculated NTT value thatis a good average indicating crossing time of the region, no matterwhich one of the inner roads is traveled along by the vehicle.

[0079] In the next part of Step (d) of method/system 10, individual NTTvalues calculated in a given processing cycle for every oriented roadsection are accumulated and statistically analyzed for providing a meanNTT value for each respective oriented road section. Eventually, morethan one NTT (statistical ‘peak’) value is identified, thereby,suggesting the possibility of different lanes having different NTTvalues. Such a situation occurs, for example, when an oriented roadsection has several lanes, each with a different NTT value.

[0080] The overall data processing of Step (d) forms a current snapshotof NTT values on part of road network 12, and, thus, on part of orientedsection network 14 (FIG. 1). The partiality of the vehicular trafficsituation picture originates from the fact that only a portion of theroads in road network 12 are monitored in every calculation cycle. Thispartial picture is fused in the next step, Step (e), with similarpartial current vehicular traffic situation pictures obtained from othersources, if any, according to a particular application of method/system10, and, with specific reports received from traffic control authoritiesand others. FIG. 5 is a schematic diagram illustrating a partial currentvehicular traffic situation picture, generally indicated as 110,featuring current NTT values, obtained from an exemplary network source62 (FIG. 2) of mobile sensor vehicular traffic data and information, andindicated for each oriented road section of an exemplary part of anoriented road section network 14 (FIG. 1).

[0081] In Step (c), there is fusing the partial current trafficsituation picture associated with each source, for generating a singlecomplete current vehicular traffic situation picture associated with theentire oriented road section network.

[0082] The partial current traffic situation picture associated witheach source, for the plurality of sources of vehicular traffic data andinformation, is fused by a Fusion and current Picture generator module3, as indicated in FIG. 1, for generating a single complete currentvehicular traffic situation picture associated with the entire orientedroad section network. Due to the generic procedures of the dataprocessing of method/system 10 (FIG. 1), representations of the partialcurrent traffic situation pictures obtained from all mobile sources 62are similar. Vehicular traffic data and information acquired from othertypes of sources, for example, acquired from sources 60 of fixedsensors, sources 64 of traffic reports by police or radio broadcasts ofvehicular traffic data and information, and, sources 66, of other typesof vehicular traffic data and information, are respectively processed.The acquiring of vehicular traffic data and information from the varietyof sources, and integrating or fusing, by way of Fusion and currentPicture generator software module 3, into one coherent or completesingle current vehicular traffic situation picture is done in regulartime intervals. For example, in a non-limiting way, the sequence ofSteps (b)-(e) is performed at a pre-determined frequency in a range offrom about once per every two minutes to about once per every tenminutes.

[0083] ‘Final’ NTT values for each of the oriented road sections isobtained by integrating or fusing NTT values associated with all of theindividual sources. NTT values associated with each source are weighedwith a confidence factor appropriate for each respective source, whereconfidence factors are determined by source parameters, such as locationaccuracy, and, quality and quantity of sensors per source. This currentvehicular traffic situation

[0084] In the first procedure, gaps are filled in using NTT values thatare predicted, for those missing oriented road sections, in previouscycles of the process, that is, in previous time intervals of performingSteps (b)-(e) of method/system 10. In the initial cycles of the process,some default values are determined by the analysis of historicalvehicular traffic data and information, and road types. It is to beemphasized, that this stage of the overall process is continuouslyperformed, and therefore, each single complete current vehicular trafficsituation picture associated with the entire oriented road sectionnetwork is based on the combination of predictions and new vehiculartraffic data and information.

[0085] In the second procedure, gaps in the current partial vehiculartraffic situation picture are filled in by using a set of vehiculartraffic rules for describing the interdependence, interrelation, andmutual correlation of vehicular traffic parameters among the pluralityof oriented road sections in a particular region. Some of the vehiculartraffic rules supply default values of the vehicular traffic parameters.The vehicular traffic rules are derived by, and based on, analyzinghistorical vehicular traffic data and information.

[0086] In Step (f), there is predicting a future complete vehiculartraffic situation picture associated with the entire oriented roadsection network.

[0087] Predicting a future complete vehicular traffic situation pictureassociated with the entire oriented road section network is performed bya Predictor software module 4, as indicated in FIG. 1. From the previousstep, Step (e), the single complete current vehicular traffic situationpicture associated with the entire oriented road section network, whichis obtained by fusing the partial current traffic situation pictureassociated with each source, for the plurality of sources of vehiculartraffic data and information, serves as a baseline or starting point forpredicting future layers of vehicular traffic situation pictures. Step(f) is performed at a pre-determined frequency in a range of from aboutonce per every two minutes to about once per every ten minutes.Typically, for implementation, in a non-limiting fashion, the frequencyof performing this step is usually less than the frequency of performingSteps (b)-(e).

[0088] The concept of the model of the comprehensive oriented roadsection network 14 (FIG. 1), dictates the process and the set ofprediction tools for implementing method/system 10. Forming currentvehicular traffic situation pictures, and forecasting or predictingfuture vehicular traffic situation pictures, are determined with the aidof vehicular traffic behavior patterns and rules, which are generated bya Patterns and rules generator module 6, as indicated in FIG. 1.

[0089] Vehicular traffic behavior patterns feature behavior rules ofindividual oriented road sections and correlation rules among theplurality of different oriented road sections, of the entire orientedroad section network 14. Accordingly, by using both types of rules, thestep of predicting a future complete vehicular traffic situation pictureassociated with a particular oriented road section in a region withinthe entire oriented road section network, is influenced by both thecurrent vehicular traffic situation picture associated with thatparticular oriented road section, and, by the plurality of currentvehicular traffic situation pictures associated with other oriented roadsections in the same region of the entire oriented road section network.

[0090]FIG. 6 is a graph, general indicated as graph 120, illustrating anexample of vehicular traffic behavior associated with an exemplaryoriented road section, in terms of different NTT values plotted as afunction of time. Information featured in FIG. 6 demonstrates an exampleof an event identification and its influence on prediction of a futurevehicular traffic situation picture. The vehicular traffic behavior isassociated with an exemplary oriented road section out of historicalvehicular traffic data and information. When compared to calculated NTTvalues, indicated by hollow circles, determined from actual data, asignificant discrepancy is observed. In this example, calculated NTTvalues are significantly higher than pattern NTT values, indicated bysolid squares, determined from the vehicular traffic pattern based onhistorical vehicular traffic data and information. The resultingcorrection prediction NTT values, associated with the exemplary orientedroad section, are indicated by solid triangles. The correctionprediction NTT values are the NTT values that will be incorporated andfused into layers of future vehicular traffic situation pictures,associated with this particular oriented road section, for the‘prediction horizon’ time period. These layers are permanently correctedwith the formation of the current vehicular traffic situation picturesin following processing cycles.

[0091] The continuously updated comprehensive and complete currentvehicular traffic situation picture serves as a baseline of athree-dimensional vehicular traffic forecast picture, where thehorizontal plane represents the roadmap and the vertical axis representsprogression of time. The three dimensional vehicular traffic situationpicture is constructed from discreet layers of vehicular trafficsituation pictures, in time-intervals of a given processing cycle. Thelower layer vehicular traffic situation picture always corresponds tothe current time, and higher layers of vehicular traffic situationpictures correspond to future predictions.

[0092] Future time layers are produced by operating prediction toolsthat are products of the analysis of historical vehicular traffic dataand information. This analysis is an on-going processing of the incomingvehicular traffic data and information, and the main resulting tools arebehavior patterns and correlation rules. A behavior pattern of aspecific oriented road section describes the regular changing of theassociated NTT values as a function of time, for example, during minuteand hourly progression of a day. Every oriented road section has its ownbehavior pattern, and different behavior patterns describe the behaviorin different circumstances, such as different days of the week,holidays, special events, weather conditions, and so on.

[0093] A correlation rule determines the correlation and interrelationof the vehicular traffic situation picture between different orientedroad sections as a function of time. Correlation rules are mostlyif-then rules. For example, they represent the fact that ‘if’ a roadcongestion is being observed on oriented road section A, ‘then’, asimilar road congestion is expected to occur on oriented road section Bafter a certain period of time. These rules are the outcome of adata-mining operation, also known as an advanced database searchingprocedure, on the historical vehicular traffic data and information.

[0094] Oriented road sections tend to behave differently even withoutthe attachment of a certain characteristic to each vehicular trafficbehavior pattern. The observance of values of the current vehiculartraffic situation picture in respect to optional vehicular trafficbehavior patterns, and the operation of vehicular traffic patternrecognition methods determines the vehicular traffic pattern thatdescribes the behavior of the oriented road section at an instant oftime.

[0095] Furthermore, while regular situations are handled with thesetools, unexpected vehicular traffic developments are identified from thecurrent vehicular traffic situation pictures by comparing them toregular vehicular traffic behavior patterns. The set of several recentlycalculated NTT values on each oriented road section is compared to thepattern that describes the behavior of this oriented road section. Anysignificant discrepancy from the pattern activates a respectivecorrection of the NTT values predicted for this section in the nearfuture. The corrections are assuming continuous change rates and a‘back-to-normal’ return after a prediction horizon period, as previouslydescribed above and graphically illustrated in FIG. 6. If thediscrepancy identified is the result of a major event with prolongedinfluence, the acquired data in the subsequent processing cycles willprolong the correction period beyond the horizon period, until the gapbetween the regular pattern values and the acquired data is reduced.

[0096] Determination of the future behavior of individual oriented roadsections from their respective traffic behavior patterns is integratedwith the mutual horizontal influence of adjacent oriented road sectionsusing the previously described correlation rules. These rules canpredict, for example, the outcome of traffic events identified by thesaid discrepancies, and, predict their propagation in time alongadjacent, and non-adjacent, oriented road sections.

[0097] In Step (g), there is using the current vehicular trafficsituation picture and the future vehicular traffic situation picture forproviding a variety of vehicular traffic related service applications toend users.

[0098] Method/system 10 (FIG. 1) of the present invention especiallyincludes features for using the vehicular traffic data and informationfor comprehensively, yet, accurately and practicably, describing currentand predicting future vehicular traffic situations and scenarios, fromwhich the processed vehicular traffic data and information are used forproviding the variety of vehicular traffic related service applicationsto the end users. Using the current vehicular traffic situation pictureand the future vehicular traffic situation picture, obtained from theprevious processing steps of method/system 10 (FIG. 1), for providing avariety of vehicular traffic related service applications to end users,is performed by a Service engine software module 5, as indicated in FIG.1.

[0099] The unique structure of the three dimensional vehicular trafficsituation picture leads to a corresponding method of analyzing it, inorder to respond to traffic oriented queries associated with a varietyof traffic related service applications. This analysis is performed by adesignated software module, Service engine software module 5, for avariety of vehicular traffic related service applications 7, asindicated in FIG. 1. This software engine is capable of finding optimalroutes between given points, estimating travel times and initiatingalerts and route alterations when unexpected traffic events change thevehicular traffic situation picture. Any traffic related serviceapplication 7 featuring such queries can receive the necessary vehiculartraffic data and information by simply connecting to Service enginesoftware module 5 through the appropriate interface. FIG. 7 and FIG. 8show two exemplary ways in which three-dimensional vehicular trafficsituation pictures are used to enhance providing traffic related serviceapplications 7 to end users.

[0100]FIG. 7 is a graphical diagram illustrating usage of athree-dimensional vehicular traffic situation picture for providingroute recommendation types of traffic related service applications toend users. In FIG. 7, three-dimensional vehicular traffic situationpicture, generally indicated as 130, has each time-layer marked with itstime-stamp (t-axis). In a current time, represented by lowest layer 132,a query arrives from a service application 7 (FIG. 1), inquiring for thefastest route from A to B. A recommendation based on the currentvehicular traffic situation picture alone is shown by the dotted line onright hand graph 134. With the three-dimensional prediction model, aparallel recommendation analyzes the alternatives in a way that everyparticular oriented road section is chosen in respect to the predictedvehicular traffic situation ‘at the time of passage of that particularoriented road section’. The choice of the oriented road section is afunction both of its predicted NTT value and the confidence factor ofthat prediction. This last factor is determined for oriented roadsections according to the regularity, the stability, and, thefluctuation rate of the historical behavior of each evaluated orientedroad section. Comparative results of using a static analysis and thepredictive analysis can be quite different, as shown in graph 134.

[0101]FIG. 8 is a graphical diagram illustrating usage of athree-dimensional vehicular traffic situation picture for providingtraffic alerts and alternative route recommendation types of trafficrelated service applications to end users. In FIG. 8, three-dimensionalvehicular traffic situation picture, generally indicated as 140, haseach time-layer marked with its time-stamp (t-axis). FIG. 8 shows thesame model for initiation of alerts and recommendations of alternativeroutes. The ‘unexpected’ traffic event 142 that happens at the current,lower layer, time, is way off the recommended route. However, thecorrelation rules show that the influence of unexpected traffic event142 will propagate in time, resulting in road congestion that will reachone of the oriented road sections of the recommended route ‘at the timethat the vehicle is supposed to pass unexpected traffic event 142.Therefore, an alert is sent to the driver with a recommendation of oneor more known alternative routes.

[0102] Method/system 10 of the present invention provides vehiculartraffic data and information, and, provides a variety of vehiculartraffic related service applications to end users, on the basis ofexisting infrastructure of vehicular traffic data and informationacquisition and collection, such as by using a cellular phone mobilecommunication network, and the construction of centralized dynamicvehicular traffic situation pictures, both current and future, for ageographical area within road network 12 (FIG. 1). The character of thevehicular traffic data and information and processing of it requiresunique, elaborate, and comprehensive, handling during the entiresequence of processing steps, Steps (a) through (g), as is representedin the above description.

[0103] The present invention is implemented for acquiring, modeling, andprocessing vehicular traffic data and information, in order to becompatible with a wide variety of end user types of traffic relatedservice applications. Therefore, it is a main aspect of the presentinvention that the ‘data providing mobile sensors’ are not limited tobeing those which are associated with the particular end users of suchtraffic related service applications. Hence, acquiring the vehiculartraffic data and information from the data providing network does notdepend at all on a specific group of mobile sensor device carrying endusers.

[0104] The final result of method/system 10 (FIG. 1) is, therefore, acontinuously on-going repetitive sequence of generating dynamicvehicular traffic situation pictures. The sequence is updated inpre-determined time intervals, as previously described above,preferably, in a range of between about once per every two minutes toabout once per every ten minutes. The described sequence of generatingdynamic three-dimensional vehicular traffic situation pictures serves asa highly accurate and precise data base for vehicular traffic relatedservice applications.

[0105] While the invention has been described in conjunction withspecific embodiments and examples thereof, it is evident that manyalternatives, modifications and variations will be apparent to thoseskilled in the art. Accordingly, it is intended to embrace all suchalternatives, modifications and variations that fall within the spiritand broad scope of the appended claims.

What is claimed is:
 1. A method for modeling and processing vehiculartraffic data and information, comprising the steps of: (a) transforminga spatial representation of a road network associated with the vehiculartraffic data and information into a network of spatially interdependentand interrelated oriented road sections, for forming an oriented roadsection network; (b) acquiring a variety of the vehicular traffic dataand information associated with said oriented road section network, froma variety of sources; (c) calculating a mean normalized travel time(NTT) value from a plurality of normalized travel time (NTT) values ofeach of a plurality of vehicles, normalized with respect to apre-determined distance, for each said oriented road section of saidoriented road section network using said prioritized, filtered, andcontrolled, vehicular traffic data and information associated with eachsaid source, for forming a partial current vehicular traffic situationpicture associated with each said source; and (d) fusing said partialcurrent traffic situation picture associated with each said source, forgenerating a single complete current vehicular traffic situation pictureassociated with entire said oriented road section network and with saidroad network.
 2. A method for acquiring vehicular traffic data andinformation, comprising: (a) transforming a spatial representation of aroad network associated with the vehicular traffic data and informationinto a network of spatially interdependent and interrelated orientedroad sections, for forming an oriented road section network; and (b)acquiring a variety of the vehicular traffic data and informationassociated with said oriented road section network, from a variety ofsources.
 3. A system for modeling and processing vehicular traffic dataand information, comprising: (a) a transform mechanism for transforminga spatial representation of a road network associated with the vehiculartraffic data and information into a network of spatially interdependentand interrelated oriented road sections, for forming an oriented roadsection network; (b) a normalized travel time (NTT) calculator softwaremodule for calculating a mean normalized travel time (NTT) value from aplurality of normalized travel time (NTT) values of each of a pluralityof vehicles, normalized with respect to a predetermined distance, foreach said oriented road section of said oriented road section network byusing said prioritized, filtered, and controlled, vehicular traffic dataand information associated with each said source, supplied by saidsampler software module, for forming a partial current vehicular trafficsituation picture associated with each said source; and (c) a fusion andcurrent picture generator module for fusing said partial current trafficsituation picture associated with each said source, supplied by saidnormalized travel time (NTT) calculator software module, for generatinga single complete current vehicular traffic situation picture associatedwith entire said oriented road section network and with said roadnetwork.